Copy Or View Numpy Subarray Using Boolean Indexing

Given a 2D numpy array, i.e.; import numpy as np data = np.array([ [11,12,13], [21,22,23], [31,32,33], [41,42,43], ]) I need to both create a ne

Solution 1:

First, make sure that your rows and cols are actually boolean ndarrays, then use them to index your data

rows = np.array([False, False, True, True], dtype=bool)
cols = np.array([True, True, False], dtype=bool)
data[rows][:,cols]

Explanation If you use a list of booleans instead of an ndarray, numpy will convert the False/True as 0/1, and interpret that as indices of the rows/cols you want. When using a bool ndarray, you're actually using some specific NumPy mechanisms.